FSI-VSCAcad. year: 2017/2018
The course introduces basic approaches to Soft Computing and classical methods used in the field. Practical use of the methods is demonstrated on solving simple engineering problems.
Learning outcomes of the course unit
Understanding of basic methods of Soft Computing and ability of their implementation.
The knowledge of basic relations of the optimization, statistics, graphs theory and programming.
Recommended optional programme components
Recommended or required reading
Sima,J., Neruda,R.: Theoretical questions of neural networks, MATFYZPRESS, 1996, ISBN 80-85863-18-9 (CS)
Munakata, T.: Fundamentals of the New Artificial Intelligence, Springer-Verlag New York, Inc., 1998. ISBN 0-387-98302-3 (EN)
Planned learning activities and teaching methods
The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused on practical topics presented in lectures.
Assesment methods and criteria linked to learning outcomes
Course-unit credit requirements: submitting a functional software project which uses implementation of selected AI method. Project is specified in the first seminar. Systematic checks and consultations are performed during the semester. Each student has to get through one test and complete all given tasks. Student can obtain 100 marks, 40 marks during seminars (20 for project and 20 for test; he needs at least 20), 60 marks during exam (he needs at least 30).
Language of instruction
The course objective is to make students familiar with basic resources of Soft Computing, potential and adequacy of their use in engineering problems solving.
Specification of controlled education, way of implementation and compensation for absences
The attendance at lectures is recommended, at seminars it is obligatory. Education runs according to week schedules. The form of compensation of missed seminars is fully in the competence of a tutor.
Type of course unit
26 hours, optionally
Teacher / Lecturer
1. Introduction, Soft Computing concept explanation in Artificial Intelligence.
2. Architectures and classification of neural networks. Perceptron, ADALINE.
3. Feed-forward neural networks, single- and multilayer perceptron. Learning: error back-propagation as iterative minimisation of the mean quadratic error.
4. Cluster analysis, dimensionality reduction, Principal component analysis.
5. RBF and RCE neural networks. Topologic organized neural network (competetive learning, Kohonen maps).
6. Neural networks as associative memories (Hopfield networks, BAM), behaviour, state diagram, attractors, learning.
7. LVQ neural networks, ART neural networks
8. Fuzzy sets, fuzzy logic and fuzzy numbers, Fuzzy inference. ANFIS
9. Evolutionary algorithms (genetic algorithms, evolutionary strategy, grammatical evolution, genetic programming).
10. Selected metaheuristics for optimization (HC12, Simulated anealing).
11. Swarm intelligence (PSO, ACO, DE, SOMA)
12. Deterministic chaos.
13. Hybrid approaches and aplications (neural networks, fuzzy logic, genetic algorithms sets).
26 hours, compulsory
Teacher / Lecturer
Seminars related to the lectures in the previous week.